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BC llama-params-optimizer

Complete methodology for local LLM performance optimization. Core principle: maximize context while fully covering GPU memory — find the sweet spot where GPU runs at full speed. Step-by-step 4-phase 10-step control variable testing process. Works for ALL llama.cpp / llama-server models on ANY hardware. Cases: Qwen3.5-MoE, Qwen3.6-35B, Qwen3.6-27B (2026-04-28).

ClawHub Agent Skills author: Hoperealize v3.1.0 MIT-0 5 files body ≈ 3 744 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureAI and agentsResearchtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
63
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "keywords"

Process rating: all ten parameters 51/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 3 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 88 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3744 tokens

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -2localhost URLs: will not work for another user
  • -258 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 362: enough signal without eating the budget
  • +4Structure: 46 headings
  • +3Step-by-step instructions: 88 items
  • +4Has examples (10 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.

External checks

ClawHub: clean
This is a documentation-only guide for tuning local llama.cpp performance, with disclosed localhost commands and no hidden executable behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026